More than half of all infectious disease outbreaks across the globe are zoonotic, involving pathogen spillover from animal reservoirs to humans. Ebola and other filoviruses rank among the most deadly zoonoses. Recent large outbreaks with mortality in the thousands both in humans and wildlife underscore the pressing need to better understand the factors promoting filovirus spillover. Although spillover is commonly defined as a pathogen crossing species boundaries, there are relatively few empirical studies or modeling frameworks that explicitly consider ecological boundaries across which spillover occurs. Crossing ecological boundaries involves processes that occur at many levels of organization: physiological processes at the individual level, interspecies interactions between individuals at the population level, interactions between populations of different species at the community level, and interactions between ecological communities within landscapes. Processes accelerating spillover often involve human activities such as habitat encroachment and land conversion, which are themselves ultimately driven by socioeconomic factors. In the context of Ebola and other filoviruses in Africa, we will develop the data sets, theoretical models and statistical tools needed for a general descriptive and predictive framework for spillover at ecological boundaries. Our project will follow an iterative design where results from mechanistic models are used to refine patterns that we test for empirically, and statistical models of large-scale data allow us to more realistically parameterize mechanistic models. Our work will test the generality of specific theories that so far have been applied only to a limited number of study systems. For example, ours will be among the first attempts to test the influence of Schmalhausen?s law -- an evolutionary theory that may explain the tendency for large outbreaks to occur at the edges of species ranges or during unusual weather conditions and which to date has primarily been investigated in the context of malaria -- in pathogens that rely on direct transmission. This work will demonstrate how new methods can provide unifying insight into patterns in critically important disease transmission systems and will enhance our ability to predict spillover of both filoviruses and many other zoonotic pathogens. Note that no human subjects, biohazards, or select agents will be involved in this project.